SoroushVahidi/scidocs huggingface dataset:
The knowledge pyramid: a critique of the DIKW hierarchy
(2009)

The paper evaluates the Data-Information-Knowledge-Wisdom (DIKW) Hierarchy. This hierarchy is part of the canon of information science and management. The paper considers whether the hierarchy, also known as the ‘Knowledge Hierarchy’, is a useful and intellectually desirable construct to introduce, whether the views expressed about DIKW are true and have evidence in favour of them, and whether there are good reasons offered or sound assumptions made about DIKW. Arguments are offered that the hierarchy is unsound and methodologically undesirable. The paper identifies a central logical error that DIKW makes. The paper identifies the dated and unsatisfactory philosophical positions of operationalism and inductivism as the philosophical backdrop to the hierarchy. The paper concludes with a sketch of some positive theories, of value to information science, on the nature of the components of the hierarchy: that data is anything recordable in a semantically and pragmatically sound way, that information is what is known in other literature as ‘weak knowledge’, that knowledge also is ‘weak knowledge’ and that wisdom is the possession and use, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge.
Testing of Digital Systems
(2003)

Device testing represents the single largest manufacturing expense in the semiconductor industry, costing over $40 million a year. The most comprehensive and wide ranging book of its kind, Testing of Digital Systems covers everything you need to know about this vitally important subject. Starting right from the basics, the authors take the reader through automatic test pattern generation, design for testability and built-in self-test of digital circuits before moving on to more advanced topics such as IDDQ testing, functional testing, delay fault testing, memory testing, and fault diagnosis. The book includes detailed treatment of the latest techniques including test generation for various fault modes, discussion of testing techniques at different levels of integrated circuit hierarchy and a chapter on system-on-a-chip test synthesis. Written for students and engineers, it is both an excellent senior/graduate level textbook and a valuable reference.
Understanding the Role of Social presence in Crowdfunding: Evidence from Leading U.S. And German Platforms
(2017)

As a novel opportunity to acquire capital from the masses, crowdfunding has attracted great attention in academia and practice. So far, little is known about the factors that promote the success of crowdfunding projects, however. In this paper, we examine in how far the social presence perceived on a project’s website influences the success of the respective crowdfunding project. Based on a data-driven analysis of 2.000 project websites from the largest crowdfunding platforms in the U.S. and Germany, we show that the perceived social presence has a significant influence on the success of crowdfunding projects. The obtained results indicate that using socially rich pictures and a socially rich description in the project presentation positively affects the success of a crowdfunding project. A socially rich profile page of the founder(s) in contrast appears to have a rather limited effect. The success of crowdfunding projects seems to be dependent on the participation behavior of the founder, however. Our results indicate that having backed other projects positively influences the success of one’s own initiative. The number of answered comments might have a negative effect on the success of the initiative, though.
Zero-Shot Learning Posed as a Missing Data Problem
(2017)

This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way - our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. Following the transductive setting, we leverage unlabeled data to refine the initial estimation. In experiments, our method achieves the highest classification accuracies on two popular datasets, namely, 96.00% on AwA and 60.24% on CUB.
Relationship between Variants of One-Class Nearest Neighbors and Creating Their Accurate Ensembles
(2018)

In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent. In this paper, we study one-class nearest neighbor (OCNN) classifiers and their different variants. We present a theoretical analysis to show the relationships among different variants of OCNN that may use different neighbors or thresholds to identify unseen examples of the non-target class. We also present a method based on inter-quartile range for optimizing parameters used in OCNN in the absence of non-target data during training. Then, we propose two ensemble approaches based on random subspace and random projection methods to create accurate OCNN ensembles. We tested the proposed methods on 15 benchmark and real world domain-specific datasets and show that random-projection ensembles of OCNN perform best.
Bus detection device for the blind using RFID application
(2009)

This paper outlines a bus detection mechanism for the blind in travelling from one place to another. In order to get transportation independently, the blind use auditory touched clues like walking stick or white cane. The limitation of the walking stick is that a blind person must come into close proximity with their surroundings to determine the location of an obstacle. For that basis, various devices have been developed such as the Sonicguide, the Mowat sensor, the Laser cane and the Navbelt [4]. However, these device can only assist the blind at a pedestrian crossing. Therefore, the project is aims to develop a bus detection prototype using Radio Frequency Identification (RFID) for the blind. The paper covers brief idea about the blind and RFID system, review relevant papers and summary of current research. The review of RFID system compare between families of auto-ID technology, the basic principle of RFID, the type of RFID tagging and the antenna characteristic. The summary of current research discussed about the development of prototype, the database system, the output mechanism and integration between hardware and software. Database management will provided. The information such as bus route, final destination and bus number are also provided. This paper also describes the future work intended to be done.
Association rules: Normalizing the lift
(2014)

Association rules is a popular data mining technique for discovering relations between variables in large amounts of data. Support, confidence and lift are three of the most common measures for evaluating the usefulness of these rules. A concern with the lift measure is that it can only compare items within a transaction set. The main contribution of this paper is to develop a formula for normalizing the lift, as this will allow valid comparisons between distinct transaction sets. Traffic accident data was used to validate the revised formula for lift and the result of this analysis was very strong.
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
(2017)

Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.
Effectiveness of domestic wastewater treatment using microbial fuel cells at ambient and mesophilic temperatures.
(2010)

Domestic wastewater treatment was examined under two different temperature (23+/-3 degrees C and 30+/-1 degrees C) and flow modes (fed-batch and continuous) using single-chamber air-cathode microbial fuel cells (MFCs). Temperature was an important parameter for treatment efficiency and power generation. The highest power density of 422 mW/m(2) (12.8 W/m(3)) was achieved under continuous flow and mesophilic conditions, at an organic loading rate of 54 g COD/L-d, achieving 25.8% COD removal. Energy recovery was found to depend significantly on the operational conditions (flow mode, temperature, organic loading rate, and HRT) as well as the reactor architecture. The results demonstrate that the main advantages of using temperature-phased, in-series MFC configurations for domestic wastewater treatment are power savings, low solids production, and higher treatment efficiency.
[Botulinum toxin injection techniques in the lower third and middle of the face, the neck and the décolleté: the "Nefertiti lift"].
(2009)

Although correction of the dynamic wrinkles of the upper part of the face is the major indication for botulinum toxin, there are also many possibilities for the middle and lower thirds of the face and neck. However, these injections are more delicate and require an experienced operator who has excellent knowledge of the muscles of these regions, their functions, the antagonist actions exercised on other muscles, particularly in terms of the complex equilibrium of the mouth. An excessive dose, an inappropriate injection point, or a centering mistake can all easily be responsible for undesirable side effects. However, the results obtained, often with lower doses than in the superior part of the face, can be highly satisfactory, notably in erasing bunny lines, improvement of marionette lines, peau d'orange chin, attenuation of peribuccal lines, melomental folds, correction of a gummy smile, and facial asymmetries. In the neck it is possible to reduce platysmal bands, horizontal lines, and diagonal lines of the neck and décolleté. The face contours can also be improved by the Nefertiti lift. In the mid and lower regions of the face, botulinum toxin is often a complement to other esthetic techniques, particularly filling procedures.
changepoint: An R Package for Changepoint Analysis
(2014)

One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementation of the recently proposed PELT algorithm. This article describes the search methods which are implemented in the package as well as some of the available test statistics whilst highlighting their application with simulated and practical examples. Particular emphasis is placed on the PELT algorithm and how results differ from the binary segmentation approach.
Eight-Band Antenna With A Small Ground Clearance for LTE Metal-Frame Mobile Phone Applications
(2018)

This letter presents a compact eight-band monopole antenna whose size is 70 mm × 7 mm × 6 mm for LTE metal-frame mobile phones. The mainly radiating part of the proposed antenna is the metal frame. There are three gaps with the same size of 1 mm in the frame, and these gaps divide the antenna into two parts. Besides, a match circuit is used to enlarge the bandwidth and make the lower band matched. The advantage of the proposed antenna is that eight bands can be covered with a metal frame under the condition of only a 7 mm ground clearance. A prototype is manufactured and tested. The measured –6 dB impedance bandwidths are 375 MHz (0.675–1.05 GHz) and 1.2 GHz (1.6–2.8 GHz). The LTE700, GSM850, GSM900, GSM1800, GSM1900, UMTS, LTE2300, and LTE2500 bands are covered. The measured efficiencies are 52.7%–78.7% at the lower band and 45.6%–81% at the higher band. The measured gains are 0–2.1 dBi at the lower band and 1.6–3.6 dBi at the higher band.
Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets
(2018)

The nonlinear characteristics of a photovoltaic (PV) array, maximum power point tracking of the PV inverter, presence of blocking diodes, and lower irradiation prevent the conventional protection devices to trip under certain faults and lead to reduced system efficiency and fire hazards. Moreover, the PV characteristics under certain partial shading conditions are similar to that under fault conditions. Hence, it is imperative to detect faults and differentiate faults from the partial shading condition to avoid false tripping of the system. This paper proposes a simple fault detection method using the available data of array voltage and current by means of wavelet packets. The proposed scheme is simulated using MATLAB/Simulink and is experimentally tested on a 1.6-kW $\text{4}\,\times \,\text{4}$ PV array to validate its performance.
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data
(2015)

The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).
Lung cancer cell identification based on artificial neural network ensembles
(2002)

An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.
Towards Online, Accurate, and Scalable QoS Prediction for Runtime Service Adaptation
(2014)

Service-based cloud applications are typically built on component services to fulfill certain application logic. To meet quality-of-service (QoS) guarantees, these applications have to become resilient against the QoS variations of their component services. Runtime service adaptation has been recognized as a key solution to achieve this goal. To make timely and accurate adaptation decisions, effective QoS prediction is desired to obtain the QoS values of component services. However, current research has focused mostly on QoS prediction of the working services that are being used by a cloud application, but little on QoS prediction of candidate services that are also important for making adaptation decisions. To bridge this gap, in this paper, we propose a novel QoS prediction approach, namely adaptive matrix factorization (AMF), which is inspired from the collaborative filtering model used in recommender systems. Specifically, our AMF approach extends conventional matrix factorization into an online, accurate, and scalable model by employing techniques of data transformation, online learning, and adaptive weights. Comprehensive experiments have been conducted based on a real-world large-scale QoS dataset of Web services to evaluate our approach. The evaluation results provide good demonstration for our approach in achieving accuracy, efficiency, and scalability.
Aromas of rosemary and lavender essential oils differentially affect cognition and mood in healthy adults.
(2003)

This study was designed to assess the olfactory impact of the essential oils of lavender (Lavandula angustifolia) and rosemary (Rosmarlnus officinalis) on cognitive performance and mood in healthy volunteers. One hundred and forty-four participants were randomly assigned to one of three independent groups, and subsequently performed the Cognitive Drug Research (CDR) computerized cognitive assessment battery in a cubicle containing either one of the two odors or no odor (control). Visual analogue mood questionnaires were completed prior to exposure to the odor, and subsequently after completion of the test battery. The participants were deceived as to the genuine aim of the study until the completion of testing to prevent expectancy effects from possibly influencing the data. The outcome variables from the nine tasks that constitute the CDR core battery feed into six factors that represent different aspects of cognitive functioning. Analysis of performance revealed that lavender produced a significant decrement in performance of working memory, and impaired reaction times for both memory and attention based tasks compared to controls. In contrast, rosemary produced a significant enhancement of performance for overall quality of memory and secondary memory factors, but also produced an impairment of speed of memory compared to controls. With regard to mood, comparisons of the change in ratings from baseline to post-test revealed that following the completion of the cognitive assessment battery, both the control and lavender groups were significantly less alert than the rosemary condition; however, the control group was significantly less content than both rosemary and lavender conditions. These findings indicate that the olfactory properties of these essential oils can produce objective effects on cognitive performance, as well as subjective effects on mood.
Sentiment analysis of a document using deep learning approach and decision trees
(2015)

The given paper describes modern approach to the task of sentiment analysis of movie reviews by using deep learning recurrent neural networks and decision trees. These methods are based on statistical models, which are in a nutshell of machine learning algorithms. The fertile area of research is the application of Google's algorithm Word2Vec presented by Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean in 2013. The main idea of Word2Vec is the representations of words with the help of vectors in such manner that semantic relationships between words preserved as basic linear algebra operations. The extra advantage of the mentioned algorithm above the alternatives is computational efficiency. This paper focuses on using Word2Vec model for text classification by their sentiment type.
A Hardware Design Language for Efficient Control of Timing Channels
(2014)

Information security can be compromised by leakage via low-level hardware features. One recently prominent example is cache probing attacks, which rely on timing channels created by caches. We introduce a hardware design language, SecVerilog, which makes it possible to statically analyze information flow at the hardware level. With SecVerilog, systems can be built with verifiable control of timing channels and other information channels. SecVerilog is Verilog, extended with expressive type annotations that enable precise reasoning about information flow. It also comes with rigorous formal assurance: we prove that SecVerilog enforces timing-sensitive noninterference and thus ensures secure information flow. By building a secure MIPS processor and its caches, we demonstrate that SecVerilog makes it possible to build complex hardware designs with verified security, yet with low overhead in time, space, and HW designer effort.
A comparison of Deep Learning methods for environmental sound detection
(2017)

Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically available. This work thus presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest path, library, train, etc. In total, 13 hours of stereo audio recordings are available, making this one of the largest datasets available.